Spoken Function Calling: A New Perspective on Spoken Language Understanding for Large Audio Language Models

arXiv:2608.05126 · cs.CL, cs.MM · Submitted 2026-08-05 · Read on arXiv

cs.CL, cs.MM

Submitted: 2026-08-05

Updated: 2026-09-27

Comments: ACM Multimedia 2026

Code: https://github.com/QwenAudio/FunResearch

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Spoken Function Calling — 24× in this paper
• Accuracy — 15× in this paper · explained in 1 episode
• Benchmark — 14× in this paper · explained in 2 episodes
• Spoken Language Understanding — 11× in this paper
• Generalization — 8× in this paper · explained in 14 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Semantic Understanding — 7× in this paper · explained in 3 episodes
• GRPO — 6× in this paper · explained in 6 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Core — 5× in this paper · explained in 2 episodes
• Generator — 4× in this paper · explained in 2 episodes
• Hallucination — 4× in this paper · explained in 4 episodes
• Multi-Agent System — 4× in this paper · explained in 7 episodes
• Robustness — 4× in this paper · explained in 6 episodes
• Ablation Study — 3× in this paper · explained in 3 episodes
• In-Context Learning — 3× in this paper · explained in 7 episodes
• Language Models (LLMs) — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 9 episodes
• Supervised Fine-Tuning — 3× in this paper
• Augmentation — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 10 episodes
• F1 Score — 2× in this paper · explained in 4 episodes
• Generalization Capabilities — 2× in this paper · explained in 2 episodes
• Transferability — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Berkeley Function Calling Leaderboard — 1× in this paper
• Catastrophic Forgetting — 1× in this paper · explained in 9 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 5 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decoder — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 10 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 11 episodes
• Memorization — 1× in this paper · explained in 4 episodes
• Model Context Protocol — 1× in this paper
• Overfitting — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 3 episodes
• Performance Gap — 1× in this paper · explained in 2 episodes
• Pragmatics — 1× in this paper · explained in 2 episodes
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 8 episodes
• Reasoning Traces — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 13 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• gpt-oss-120b & gpt-oss-20b Model Card
• Evaluating Large Language Models Trained on Code
• Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
• Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
• Scaling Laws for Neural Language Models
• API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
• ToolACE: Winning the Points of LLM Function Calling
• ART: Automatic multi-step reasoning and tool-use for large language models
• MAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark
• Defeating the Training-Inference Mismatch via FP16
• AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
• ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark
• Step-Audio 2 Technical Report
• Qwen3-Omni Technical Report
• Qwen3 Technical Report
• LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
• IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech

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